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Linearly Solvable Mean-Field Road Traffic Games
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-9940-5929
2018 (English)In: 56th Annual Allerton Conference on Communication, Control, and Computing, IEEE , 2018, p. 283-289Conference paper, Published paper (Refereed)
Abstract [en]

We analyze the behavior of a large number of strategic drivers traveling er an urban traffic network using the mean-field game framework. We sume an incentive mechanism for congestion mitigation under which each iver selecting a particular route is charged a tax penalty that is fine in the logarithm of the number of agents selecting the same ute. We show that the mean-field approximation of such a rge-population dynamic game leads to the so-called linearly solvable rkov decision process, implying that an open-loop epsilon-Nash uilibrium of the original game can be found simply by solving a nite-dimensional linear system.

Place, publisher, year, edition, pages
IEEE , 2018. p. 283-289
Series
Annual Allerton Conference on Communication Control and Computing, ISSN 2474-0195
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-248120DOI: 10.1109/ALLERTON.2018.8636077ISI: 000461021200040Scopus ID: 2-s2.0-85062891323ISBN: 978-1-5386-6596-1 (print)OAI: oai:DiVA.org:kth-248120DiVA, id: diva2:1307213
Conference
56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018; Allerton Park and Retreat CenterMonticello; United States; 2 October 2018 through 5 October 2018
Note

QC 20190426

Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-04-26Bibliographically approved

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Nekouei, EhsanJohansson, Karl H.

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